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arXiv 2024年

作者： Li, Yuanzhe Wu, Yue Zhong, Muyao Liu, Shengcai Yang, Peng Department of Statistics and Data Science Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Statistics and Data Science Department of Computer Science and Engineering Southern University of Science and Technology China

Financial market simulation (FMS) serves as a promising tool for understanding market anomalies and underlying trading behaviors. To ensure high-fidelity simulations, it is crucial to calibrate the FMS model to generate data closely resembling the observed market data. Previous efforts primarily focused on calibrating merely the mid-price data, leading to essential information loss of the market activities and thus biasing the calibrated model. The Limit Order Book data (LOB) is the fundamental data that fully captures the market microstructure and is adopted by exchanges worldwide. However, LOB is not applicable to existing calibration objective functions due to its unique properties, including high dimensionality, differences in feature scales, and cross-feature constraints. This paper proposes an efficient Transformer-based autoencoder to explicitly learn the vectorized representations of LOB, addressing its unique challenges. The resulted latent vector, which captures the major information of LOB, can then be applied for calibration. Extensive experiments show that the learned latent representation preserves not only the non-linear auto-correlation in the temporal axis but also the precedence between successive price levels of LOB. Besides, we verify that the representation learning aligns with the downstream calibration tasks, making the first advancement in calibrating FMS on LOB. © 2024, CC BY.

关键词： Financial markets

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Harvesting random embedding for high-frequency change-point detection in temporal complex systems

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National science Review 2022年第4期9卷 92-104页

作者： Jia-Wen Hou Huan-Fei Ma Dake He Jie Sun Qing Nie Wei Lin Research Institute of Intelligent Complex Systems Fudan University Centre for Computational Systems Biology Institute of Science and Technology for Brain-Inspired IntelligenceFudan University School of Mathematical Sciences Soochow University Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine School of Mathematical Sciences and Shanghai Center for Mathematical Sciences Fudan University Department of Mathematics Department of Developmental and Cell Biologyand NSF-Simons Center for Multiscale Cell Fate ResearchUniversity of California Shanghai Key Laboratory for Contemporary Applied Mathematics LNMS (Fudan University)and LCNBI (Fudan University) State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceInstitutes of Brain ScienceFudan University

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and model-free method to achieve the mission based merely on the time-series data recorded from real-world systems. Here, we develop such a model-free approach, named temporal change-point detection(TCD), and integrate both dynamical and statistical methods to address this important challenge in a novel way. The proposed TCD approach, basing on exploitation of spatial information of the observed time series of high dimensions, is able not only to detect the separate change points of the concerned systems without knowing, a priori, any information of the equations of the systems, but also to harvest all the change points emergent in a relatively high-frequency manner, which cannot be directly achieved by using the existing methods and *** effectiveness is comprehensively demonstrated using the data from the representative complex dynamics and real-world systems from biology to geology and even to social science.

关键词： temporal systems time series change-point detection complex dynamical systems

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Annotating TSSs in Multiple Cell Types Based on DNA Sequence and RNA-seq Data via DeeReCT-TSS

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Genomics, Proteomics & Bioinformatics 2022年第5期20卷 959-973页

作者： Juexiao Zhou Bin Zhang Haoyang Li Longxi Zhou Zhongxiao Li Yongkang Long Wenkai Han Mengran Wang Huanhuan Cui Jingjing Li Wei Chen Xin Gao Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Computational Bioscience Research Center King Abdullah University of Science and TechnologyThuwal 23955-6900Saudi Arabia Department of Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Shenzhen Key Laboratory of Gene Regulation and Systems Biology School of Life SciencesSouthern University of Science and TechnologyShenzhen 518055China Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China

The accurate annotation of transcription start sites(TSSs)and their usage are critical for the mechanistic understanding of gene regulation in different biological *** fulfill this,specific high-throughput experimental technologies have been developed to capture TSSs in a genome-wide manner,and various computational tools have also been developed for in silico prediction of TSSs solely based on genomic *** of these computational tools cast the problem as a binary classification task on a balanced dataset,thus resulting in drastic false positive predictions when applied on the genome ***,we present Dee Re CT-TSS,a deep learningbased method that is capable of identifying TSSs across the whole genome based on both DNA sequence and conventional RNA sequencing *** show that by effectively incorporating these two sources of information,Dee Re CT-TSS significantly outperforms other solely sequence-based methods on the precise annotation of TSSs used in different cell ***,we develop a meta-learning-based extension for simultaneous TSS annotations on 10 cell types,which enables the identification of cell type-specific ***,we demonstrate the high precision of DeeReCT-TSS on two independent datasets by correlating our predicted TSSs with experimentally defined TSS chromatin *** source code for Dee Re CT-TSS is available at https://github.-com/Joshua Chou2018/Dee Re CT-TSS_release and https://***/biocode/tools/BT007316.

关键词： Transcription start site Machine learning Deep learning Meta-learning RNA sequencing

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VarAD: Lightweight High-Resolution Image Anomaly Detection via Visual Autoregressive Modeling

arXiv

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arXiv 2024年

作者： Cao, Yunkang Yao, Haiming Luo, Wei Shen, Weiming The State Key Laboratory of Intelligent Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The State Key Laboratory of Precision Measurement Technology and Instruments Department of Precision Instrument Tsinghua University Beijing100084 China

This paper addresses a practical task: High-Resolution Image Anomaly Detection (HRIAD). In comparison to conventional image anomaly detection for low-resolution images, HRIAD imposes a heavier computational burden and necessitates superior global information capture capacity. To tackle HRIAD, this paper translates image anomaly detection into visual token prediction and proposes VarAD based on visual autoregressive modeling for token prediction. Specifically, VarAD first extracts multi-hierarchy and multi-directional visual token sequences, and then employs an advanced model, Mamba, for visual autoregressive modeling and token prediction. During the prediction process, VarAD effectively exploits information from all preceding tokens to predict the target token. Finally, the discrepancies between predicted tokens and original tokens are utilized to score anomalies. Comprehensive experiments on four publicly available datasets and a real-world button inspection dataset demonstrate that the proposed VarAD achieves superior high-resolution image anomaly detection performance while maintaining lightweight, rendering VarAD a viable solution for HRIAD. Code is available at https://***/caoyunkang/VarAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

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Attention Information Fusion Emotion Analysis Model Based on BiLSTM-BERT

Attention Information Fusion Emotion Analysis Model Based on...

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Consumer Electronics and computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Zhi-Wei Cheng Shun-Xin Li Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial Wuhan University of Science and Technology Wuhan China

Aiming at the problem that most sentiment analysis methods do not focus on the weight fusion of local context and global context information and the important information in fusion features is not prominent, AIFM-BB (Attention information fusion emotion analysis model based on BiLSTM-BERT) is proposed. The model uses a pre-trained BERT model to process text vectors and a collaborative speech analysis algorithm to process audio vectors, and introduces BiLSTM and Attention mechanisms. The weight of the feature vector is dynamically adjusted by combining the global information and local information of text and audio and the attention information of text and audio modes. The emotion analysis results are obtained through the Softmax layer after the fusion features are self-focused. The experimental results show that the accuracy of the proposed AIFM-BB model on the CMU-MOSI dataset is 85.3%, which is 2.8 percentage points higher than the baseline. In the multi-category emotion classification task, the accuracy of the model is 44.7%, an improvement of 1.1 percentage points compared with baseline. The accuracy is 83.6% on the CMU-MOSEI dataset, an improvement of 1.4 percentage points compared to baseline.

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Self-Supervised Reflective Learning Through Self-Distillation and Online Clustering for Speaker Representation Learning

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1535-1550页

作者： Danwei Cai Zexin Cai Ze Li Ming Li Department of Electrical and Computer Engineering Duke University Durham NC USA Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Digital Innovation Research Center Duke Kunshan University Kunshan China School of Computer Science Wuhan University Wuhan China

Speaker representation learning is crucial for voice recognition systems, with recent advances in self-supervised approaches reducing dependency on labeled data. Current two-stage iterative frameworks, while effective, suffer from significant computational overhead due to repeated rounds of clustering and training. They also struggle with noisy pseudo labels that can impair model learning. This paper introduces self-supervised reflective learning (SSRL), an improved framework that addresses these limitations by enabling continuous refinement of pseudo labels during training. Through a teacher-student architecture and online clustering mechanism, SSRL eliminates the need for iterative training rounds. To handle label noise, we incorporate noisy label modeling and pseudo label queues that maintain temporal consistency. Experiments on VoxCeleb show SSRL's superiority over current two-stage iterative approaches, surpassing the performance of a 5-round method in just a single training round. Ablation studies validate the contributions of key components like noisy label modeling and pseudo label queues. Moreover, consistent improvements in pseudo labeling and the convergence of cluster counts demonstrate SSRL's effectiveness in deciphering unlabeled data. This work marks an important advancement in efficient and accurate self-supervised speaker representation learning through the novel reflective learning paradigm.

关键词： Training Representation learning Iterative methods Noise Adaptation models Noise measurement Predictive models Speech processing Robustness Convergence

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Fixed-time adaptive parameter estimation for magnetostrictive actuated positioning system

Fixed-time adaptive parameter estimation for magnetostrictiv...

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第43届中国控制会议

作者： Shengbin Wu Jing Na Yingbo Huang Faxiang Zhang Xinkai Chen Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Yunnan Key Laboratory of Intelligent Control and Application Department of Electronic and Information Systems Shibaura Institute of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

An adaptive parameter estimation method that ensures fixed-time convergence is proposed for micro-nano positioning system driven by magnetostrictive actuator(i.e., magnetostrictive actuated positioning system). Firstly, the hysteresis nonlinearity of the magnetostrictive actuated positioning system is characterized using the generalized Prandtl–Ishlinskii(GPI) ***, the low-pass filter is introduced to design the adaptive estimation law driven by the estimation error, by which the unmeasured intermediate hysteresis variable can be avoided, and the estimation error converges to zero within a fixed time. Specifically,a modified error variable is designed to deal with the influence of the regression vector, thereby improving convergence performance and simplifying parameter adjustment. Finally, the effectiveness of the presented method is verified by simulation and experimental outcomes.

关键词： Adaptive parameter estimation Fixed-time convergence Magnetostrictive actuator Micro/nano positioning system

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Asynchronous Deep Reinforcement Learning for Collaborative Task Computing and On-Demand Resource Allocation in Vehicular Edge Computing

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IEEE Transactions on intelligent Transportation systems 2023年第12期24卷 15513-15526页

作者： Liu, Lei Feng, Jie Mu, Xuanyu Pei, Qingqi Lan, Dapeng Xiao, Ming Xidian University Guangzhou Institute of Technology Guangzhou510555 China Ministry of Education Shanghai201804 China Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunication Engineering Shaanxi Xi'an710071 China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an710121 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China University of Oslo Department of Informatics Oslo0373 Norway Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm10044 Sweden

Vehicular Edge Computing (VEC) is enjoying a surge in research interest due to the remarkable potential to reduce response delay and alleviate bandwidth pressure. Facing the ever-growing service applications in VEC, how to effectively aggregate and flexibly schedule ubiquitous network resources for implementing diverse tasks and meeting differentiated demands from numerous vehicular users remains haunting. Toward this end, we investigate collaborative task computing and on-demand resource allocation. The collaborative computing framework in VEC is provided to support deep collaboration and intelligent management of heterogeneous resources widely distributed in vehicles, edge servers and cloud. Based on this framework, the joint optimization problem of distributed task offloading and multi-resource management is formulated with the aim to maximize the system utility by making the optimal task and resource scheduling policy, the novelty of which lies in the exploration of available vehicle resources and the consideration of service migration. In view of the dynamics, randomness and time-variant of vehicular networks, the asynchronous deep reinforcement algorithm is leveraged to find the optimal solution. Extensive simulation experiments are implemented to demonstrate the superiority of our proposed algorithm in terms of response latency compared with full offloading and random offloading. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Ferroelectricity of pristine Hf_(0.5)Zr_(0.5)O_(2) films fabricated by atomic layer deposition

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Chinese Physics B 2023年第10期32卷 684-688页

作者：陈璐秋张晓旭冯光迪刘逸飞郝胜兰朱秋香冯晓钰屈可杨振中祁原深 Yachin Ivry Brahim Dkhil 田博博褚君浩段纯刚 Key Laboratory of Polar Materials and Devices(MOE) Ministry of EducationShanghai Center of Brain-inspired Intelligent Materials and DevicesDepartment of ElectronicsEast China Normal UniversityShanghai 200241China Zhejiang Laboratory Hangzhou 310000China Guangdong Provisional Key Laboratory of Functional Oxide Materials and Devices Southern University of Science and TechnologyShenzhen 518055China Department of Materials Sciences and Engineering Guangdong Technion-Israel Institute of TechnologyShantou 515063China Department of Materials Science and Engineering Solid-State InstituteTechnion-Israel Institute of TechnologyHaifa 3200003Israel UniversitéParis-Saclay CentraleSupélecCNRS-UMR 8580Laboratoire StructuresPropriétés et Modélisation des SolidesGif-sur-Yvette 91190France State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Institute of Optoelectronics Fudan UniversityShanghai 200433China Collaborative Innovation Center of Extreme Optics Shanxi UniversityShanxi 030006China

Hafnium-based ferroelectric films,remaining their ferroelectricity down to nanoscale thickness,present a promising application for low-power logic devices and nonvolatile *** has been appealing for researchers to reduce the required temperature to obtain the ferroelectric phase in hafnium-based ferroelectric films for applications such as flexible and wearable *** work demonstrates that a remanent polarization(P_(r))value of>5μC/cm^(2)can be obtained in asdeposited Hf_(0.5)Zr_(0.5)O_(2)(HZO)films that are fabricated by thermal atomic layer deposition(TALD)under low temperature of 250℃.The ferroelectric orthorhombic phase(o-phase)in the as-deposited HZO films is detected by scanning transmission electron microscopy(STEM).This low fabrication temperature further extends the compatibility of ferroelectric HZO films to flexible electronics and avoids the cost imposed by following high-temperature annealing treatments.

关键词： Hf_(0.5)Zr_(0.5)O_(2)(HZO) ferroelectric orthorhombic without annealing

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An Electromyographic Signal Acquisition System for Sarcopenia 1

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5th International Workshop on Internet of Things of Big Data for Healthcare, IoTBDH 2023

作者： Jian, Yihui Mao, Kaitai Chen, Jing Ling, Xinrui Jin, Ziguan Ye, Zhiqiu Yang, Geng Zhang, Qin Xu, Kaichen State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310000 China School of Ocean Science and Engineering Harbin Institute of Technology Weihai China Department of Geriatrics First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Key Laboratory of Diagnosis and Treatment of Aging and Physic-Chemical Injury Diseases of Zhejiang Province School of Medicine The First Affiliated Hospital Zhejiang University Zhejiang Hangzhou China Zhejiang Key Laboratory of Intelligent Operation and Maintenance Robot Hangzhou China

ISBN: (数字)9783031522161

ISBN: (纸本)9783031522154

Sarcopenia, characterized by age-related loss of strength and muscle mass, profoundly affects the elderly’s well-being. Currently, diagnosing sarcopenia requires the utilization of extensive and cumbersome testing equipment. Surface electromyography (sEMG) is able to track muscle status and has been effectively employed in diagnosing various diseases or human-machine interactions, but its application in sarcopenia detection remains unexplored. In this study, a compact sEMG system is developed to monitor sarcopenia, which is endowed with compact dimensions, ease of operation, and wireless data transmission capabilities. The investigation involves the analysis of sEMG signals from 15 elderly participants. A significant correlation is established between the median frequency of these sEMG signals and the appendicular skeletal muscle mass (ASM). The proposed sEMG-based sarcopenia detection device exhibits the merit in portable use and highly operational efficiency. In future, it holds a high potential to replace the commercially cumbersome or non-portable testing devices, thereby facilitating widespread sarcopenia screening across diverse populations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Muscle

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